Search

The problem of state tracking with active observation control is considered for a system modeled by a discrete-time, finite-state Markov chain observed through conditionally Gaussian measurement vectors. The measurement model statistics are shaped by the underlying state and an exogenous control input, which influence the observations' quality. Exploiting an innovations approach, an approximate minimum mean-squared error (MMSE) filter is derived to estimate the Markov chain system state. To optimize the control strategy, the associated mean-squared error is used as an optimization criterion in a partially observable Markov decision process formulation. A stochastic dynamic programming algorithm is proposed to solve for the optimal solution. To enhance the quality of system state estimates, approximate MMSE smoothing estimators are also derived. Finally, the performance of the proposed framework is illustrated on the problem of physical activity detection in wireless body sensing networks. The power of the proposed framework lies within its ability to accommodate a broad spectrum of active classification applications, including sensor management for object classification and tracking, estimation of sparse signals, and radar scheduling.

Forest and land fires in Indonesia have serious impacts on many aspects, including the environment, health, economy, politics, and international relations. They cause haze pollution that extends to neighboring countries and peatland degradation. Despite extensive research and mitigation efforts, forest and land fires continue to occur and cost lives. Therefore, effective management and mitigation strategies are required. This research developed a web-based knowledge management system (KMS) using the Laravel framework as an effective forest and land fire mitigation platform. The KMS aims to support decision-making, facilitate knowledge exchange, improve coordination between stakeholders, and expand access to relevant information, while maintaining the sustainability of forest and land resources in Indonesia. The KMS evaluation results cover two important aspects: blackbox evaluation and performance evaluation. The blackbox evaluation showed that KMS provides knowledge retrieval features based on expert knowledge. The performance evaluation revealed that the KMS provides easy and quick access to information on forest and land fire prevention and management. Thus, this research has great potential to help overcome the problem of forest and land fires in Indonesia and protect the environment and society from their adverse effects.

The maximum-likelihood (ML) sequence detection problem for channels with memory is investigated. The Viterbi algorithm (VA) provides an exact solution. Its computational complexity is linear in the length of the transmitted sequence, but exponential in the channel memory length. On the other hand, the sphere decoding (SD) algorithm also solves the ML detection problem exactly, and has expected complexity which is a low-degree polynomial (often cubic) in the length of the transmitted sequence over a wide range of signal-to-noise ratios. We combine the sphere-constrained search strategy of SD with the dynamic programming principles of the VA. The resulting algorithm has the worst-case complexity determined by the VA, but often significantly lower expected complexity. Index Terms--Expected complexity, frequency-selective channels, maximum-likelihood (ML) sequence detection, sphere decoding, Viterbi algorithm (VA).

With the COVID-19 pandemic, human activities have been disrupted, including the learning process in the education sector. The government issued a policy that learning is online. The online learning method requires to master the use of technology and it is still considered new by most teachers, that’s why this community service is carried out. This activity is intended for all teachers and teaching staff at MTs Muhammadiyah 02 Pekanbaru and Activities are carried out with three processes, namely preparation, implementation, and evaluation. The preparation stage is carried out by meeting with the school to determine the topic, schedule, and timing. The implementation stage is carried out by presenting and practicing the use of the Zoom application directly. The evaluation stage is carried out by helping to receive complaints about the use of applications from participants after the activity is carried out. The results of community service activity can increase the ability to use the Zoom application so that the online learning process becomes effective and maximized.